Ranganathan Rani Hemamalini

The most prominent source of drinking water is groundwater, followed by lakes and reservoirs. Hydrological parameters like temperature, dissolved oxygen, pH, conductivity, ORP, and turbidity often change due to waste dumping into natural drinking water sources, particularly in densely populated areas. As a result, the water quality must be tested before public consumption to ensure healthy living in society. This research collected water samples from 129 wells in the Kanchipuram district in Tamil Nadu, India. An efficient integrated machine-learning-based prediction model has been proposed and modeled to determine the groundwater quality index (GQI). Several machine learning models were used to predict the water’s quality, including the naïve Bayes model, the KNN classifier, and the XGBoost classifier. Water quality predictions in 2024 were made using a combination of classification algorithms and models based on long short-term memory (LSTM) neural networks. The projected water quality characteristics were analyzed using geographical information system (GIS) technology to better understand and visualize the results. The XGBoost classifier model outperforms prior findings in the literature, with an accuracy of roughly 94.6%. The classification and prediction model was validated using collected and tested current data samples from a selected well. The findings were accurate within the 5% error range, promoting sustainability.

Air pollution has become a global health issue, affecting millions of people annually. It is essential to obtain accurate and up-to-date information on air quality levels to reduce the impact of air pollution on human health. Traditional air quality monitoring methods are limited by spatial coverage and the time required for sample collection and analysis. However, the Internet of Things (IoT), drone technology, and Geographic Information System (GIS) technology have enabled the development of real-time air quality monitoring systems that provide accurate and up-to-date information on air quality levels across large areas. The study found that gas concentration varied significantly at different heights, with the highest concentration at 3 m from the ground and the lowest at 9 m. The concentration of gases also varied by month, with the highest concentration of CO recorded in March at 45 ppm at 3 m, and the highest concentration of NH₄ recorded in September at 66.7 ppm at 3 m. Kodungaiyur dump yard needs environmental monitoring due to the high levels of environmental pollution and health risks caused by unsegregated waste. In conclusion, developing real-time air quality monitoring systems using GIS technology is necessary to obtain accurate and up-to-date information on air quality levels. It is essential to monitor the Kodungaiyur dump yard and other, similar sites regularly to prevent the health risks associated with environmental pollution.

Anaerobic absorption is a sequence of processes by which microbes separate decomposable wastes without oxygen. They convert the decomposable wastes in the form of carbon dioxide and water. Due to the nonlinear nature of the anaerobic process several control strategies are used to adjust the manipulated inputs to maintain the process outputs at desired set points. The objective of this work is to achieve the desired sludge temperature level of the anaerobic digester using PI (Proportional Integral), PID (Proportional-Integral-Derivative) Control and Fuzzy Logic Control (FLC). The comparative study indicates that the Fuzzy Logic Control is enhanced over conventional PID Control. The designed system can also be implemented using BFOA (Bacterial Foraging Optimization Algorithm) to optimize the operation and in tuning of PID controller parameters for an anaerobic digester.

Trends in VLSI technology represents bottleneck for the future high performance computing architectures as the ratio of the power pins to the total package pins keeps on increasing. A viable solution to this is bottleneck is to have final power consumption on-chip. Switched Capacitor DC-DC Converters are the most preferred for on-chip power conversion. However, as the number of power conversion modules increases and they get distributed across the chip area, clock distribution for the switched capacitor converters becomes a non-trivial task and the increased interconnect lengths cause clock degradation and power dissipation. This paper presents a power efficient signaling topology for driving the clocks to higher interconnect lengths.